Two-stage Methods   Early methods in Novel Class Discovery [1]}, [2]}, [3]} operate in a phased setting. In the first phase, the model learns from the labeled data, and in the subsequent phase, it discover novel categories from the unlabeled pool. MCL [2]} and KCL [1]} learn a binary similarity function using meta-learning in the first phase, and use this in the category discovery phase. DTC [3]} first learns a feature extractor on the labeled data. In the next stage, these features are used to initialise a clustering algorithm [7]}, which further fine-tunes these representations using the unlabeled data, thereby improving class discovery.
